Metabolomics plays an indispensable role in the growing systems biology approaches to identify reliable cancer biomarkers. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been extensively used for high-throughput comparison of the levels of thousands of metabolites among biological samples. However, the potential values of many disease-associated analytes discovered by these platforms have been inadequately explored in systems biology research due to lack of computational tools. Partly due to these limitations, poor reproducibility of previously identified metabolite biomarker candidates has been observed, especially when they are evaluated through independent platforms and validation sets. This project aims to address this challenge using a new software tool (SysMet) that utilizes network-based approaches for: (1) prioritizing putative IDs to assist in metabolite identification; (2) performing differential analysis to uncover relationships between disease and metabolites by investigating the rewiring and conserved interactions among metabolites in the progression of the disease; and (3) integrating metabolomic data with transcriptomic, proteomic, and glycomic data to identify highly promising metabolites as biomarker candidates. The tool will contribute to improving the ability of researchers to discover more reliable biomarkers by enhancing the role of metabolomics in systems biology research.